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WillLoki/distilbert-base-uncased-lora-text-classification

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face
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distilbert-base-uncased-lora-text-classification

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.898124
  • —Accuracy: {'accuracy': 0.893}

Model description

Using LoRA to fine-tune distilbert/distilbert-base-uncased to classify movie reviews

Training and evaluation data

https://huggingface.co/datasets/stanfordnlp/imdb

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • —learning_rate: 0.001
  • —trainbatchsize: 4
  • —evalbatchsize: 4
  • —seed: 42
  • —optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
  • —lrschedulertype: linear
  • —num_epochs: 10

Training results

Training LossEpochStepValidation LossAccuracy
No log1.02500.7278{'accuracy': 0.833}
0.3582.05000.6268{'accuracy': 0.852}
0.3583.07500.6568{'accuracy': 0.872}
0.18734.010000.7663{'accuracy': 0.883}
0.18735.012500.7704{'accuracy': 0.877}
0.04376.015000.8981{'accuracy': 0.893}
0.04377.017500.9872{'accuracy': 0.886}
0.01488.020001.0022{'accuracy': 0.888}
0.01489.022501.0471{'accuracy': 0.892}
0.000610.025001.0335{'accuracy': 0.889}

Framework versions

  • —PEFT 0.14.0
  • —Transformers 4.48.3
  • —Pytorch 2.5.1+cu124
  • —Datasets 3.3.2
  • —Tokenizers 0.21.0